15 openstreetmap

Downloading OpenStreetMap data with a single line of code

Uncomment the following line to install leafmap if needed.

In [1]:

# !pip install geopandas osmnx leafmap

# !pip install geopandas osmnx leafmap

In [2]:

import leafmap

import leafmap

Add OSM data of place(s) by name or ID to the map. Note that the leafmap custom layer control does not support GeoJSON, we need to use the ipyleaflet built-in layer control.

In [3]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("New York City", layer_name='NYC')
m

m = leafmap.Map(toolbar_control=False, layers_control=True) m.add_osm_from_geocode("New York City", layer_name='NYC') m

/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

Out[3]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [4]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("Chicago, Illinois", layer_name='Chicago, IL')
m

m = leafmap.Map(toolbar_control=False, layers_control=True) m.add_osm_from_geocode("Chicago, Illinois", layer_name='Chicago, IL') m

/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add OSM entities within boundaries of geocodable place(s) to the map.

In [5]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
place = "Bunker Hill, Los Angeles, California"
tags = {"building": True}
m.add_osm_from_place(place, tags, layer_name="Los Angeles, CA")
m

m = leafmap.Map(toolbar_control=False, layers_control=True) place = "Bunker Hill, Los Angeles, California" tags = {"building": True} m.add_osm_from_place(place, tags, layer_name="Los Angeles, CA") m

/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

2022-01-22 15:10:54 Configured OSMnx 1.1.2
2022-01-22 15:10:54 HTTP response caching is on
2022-01-22 15:10:54 Retrieved response from cache file "cache/8d8366ad6b96fa854a9d6d038c022b8bdcdb6fe6.json"
2022-01-22 15:10:54 Created GeoDataFrame with 1 rows from 1 queries
2022-01-22 15:10:54 Constructed place geometry polygon(s) to query API
2022-01-22 15:10:54 Projected GeoDataFrame to +proj=utm +zone=11 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:10:54 Projected GeoDataFrame to epsg:4326
2022-01-22 15:10:54 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:10:54 Retrieved response from cache file "cache/3063abeb36c57d92b80fe38fd9a33fcf0e02c542.json"
2022-01-22 15:10:54 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:10:54 Converting 1430 elements in JSON responses to geometries
2022-01-22 15:10:54 77 geometries created in the dict
2022-01-22 15:10:54 13 untagged geometries removed
2022-01-22 15:10:54 Created r-tree spatial index for 64 geometries
2022-01-22 15:10:54 Identified 64 geometries inside polygon
2022-01-22 15:10:54 0 geometries removed by the polygon filter
2022-01-22 15:10:54 8 geometries removed by the tag filter
2022-01-22 15:10:54 56 geometries in the final GeoDataFrame

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Show OSM feature tags.

https://wiki.openstreetmap.org/wiki/Map_features

In [6]:

# leafmap.osm_tags_list()

# leafmap.osm_tags_list()

Add OSM entities within some distance N, S, E, W of address to the map.

In [7]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_address(
    address="New York City", 
    tags={"amenity": "bar"}, 
    dist=1500,
    layer_name="NYC bars")
m

m = leafmap.Map(toolbar_control=False, layers_control=True) m.add_osm_from_address( address="New York City", tags={"amenity": "bar"}, dist=1500, layer_name="NYC bars") m

2022-01-22 15:10:55 Pausing 1 seconds before making HTTP GET request
2022-01-22 15:10:56 Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=New+York+City with timeout=180
2022-01-22 15:10:56 Resolved nominatim.openstreetmap.org to 140.211.167.100
2022-01-22 15:10:56 Downloaded 0.5kB from nominatim.openstreetmap.org
2022-01-22 15:10:56 Saved response to cache file "cache/78258c318356c20c380e1fed844176f90c053cf7.json"
2022-01-22 15:10:56 Geocoded "New York City" to (40.7127281, -74.0060152)
2022-01-22 15:10:56 Created bbox 1500 m from (40.7127281, -74.0060152): 40.72621790503239,40.69923829496761,-73.98821837587228,-74.02381202412771
2022-01-22 15:10:56 Projected GeoDataFrame to +proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:10:56 Projected GeoDataFrame to epsg:4326
2022-01-22 15:10:56 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:10:56 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:10:56 Pausing 0 seconds before making HTTP POST request
2022-01-22 15:10:56 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:10:56 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:10:59 Downloaded 37.2kB from overpass-api.de
2022-01-22 15:10:59 Saved response to cache file "cache/7d10c2e8d50f39407dda671d56290ea2808b3d4a.json"
2022-01-22 15:10:59 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:10:59 Converting 107 elements in JSON responses to geometries
2022-01-22 15:10:59 98 geometries created in the dict
2022-01-22 15:10:59 0 untagged geometries removed
2022-01-22 15:10:59 Created r-tree spatial index for 98 geometries
2022-01-22 15:10:59 Identified 98 geometries inside polygon
2022-01-22 15:10:59 0 geometries removed by the polygon filter
2022-01-22 15:10:59 0 geometries removed by the tag filter
2022-01-22 15:10:59 98 geometries in the final GeoDataFrame

Out[7]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [8]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_address(
    address="New York City", 
    tags={
        "landuse": ["retail", "commercial"],
        "building": True
    },
    dist=1000, layer_name="NYC buildings")
m

m = leafmap.Map(toolbar_control=False, layers_control=True) m.add_osm_from_address( address="New York City", tags={ "landuse": ["retail", "commercial"], "building": True }, dist=1000, layer_name="NYC buildings") m

2022-01-22 15:10:59 Retrieved response from cache file "cache/78258c318356c20c380e1fed844176f90c053cf7.json"
2022-01-22 15:10:59 Geocoded "New York City" to (40.7127281, -74.0060152)
2022-01-22 15:10:59 Created bbox 1000 m from (40.7127281, -74.0060152): 40.721721303354926,40.703734896645074,-73.99415065058152,-74.01787974941847
2022-01-22 15:10:59 Projected GeoDataFrame to +proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:10:59 Projected GeoDataFrame to epsg:4326
2022-01-22 15:10:59 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:10:59 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:00 Pausing 0 seconds before making HTTP POST request
2022-01-22 15:11:00 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27landuse%27%3D%27retail%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27landuse%27%3D%27retail%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27landuse%27%3D%27retail%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28node%5B%27landuse%27%3D%27commercial%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27landuse%27%3D%27commercial%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27landuse%27%3D%27commercial%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28node%5B%27building%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27building%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27building%27%5D%28poly%3A%2740.703735+-74.017880+40.703735+-73.994151+40.721721+-73.994151+40.721721+-74.017880+40.703735+-74.017880%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:11:00 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:10 Downloaded 2,726.6kB from overpass-api.de
2022-01-22 15:11:10 Saved response to cache file "cache/2c876f231fa35ebe028236a3fb06b51de7794ae0.json"
2022-01-22 15:11:10 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:11:10 Converting 21003 elements in JSON responses to geometries
2022-01-22 15:11:10 2962 geometries created in the dict
2022-01-22 15:11:10 74 untagged geometries removed
2022-01-22 15:11:11 Created r-tree spatial index for 2888 geometries
2022-01-22 15:11:11 Identified 2881 geometries inside polygon
2022-01-22 15:11:11 7 geometries removed by the polygon filter
2022-01-22 15:11:11 137 geometries removed by the tag filter
2022-01-22 15:11:11 2751 geometries in the final GeoDataFrame

Out[8]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add OSM entities within a N, S, E, W bounding box to the map.

In [9]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
north, south, east, west = 40.7551, 40.7454, -73.9738, -73.9965
m.add_osm_from_bbox(north, south, east, west, tags={"amenity": "bar"}, layer_name="NYC bars")
m

m = leafmap.Map(toolbar_control=False, layers_control=True) north, south, east, west = 40.7551, 40.7454, -73.9738, -73.9965 m.add_osm_from_bbox(north, south, east, west, tags={"amenity": "bar"}, layer_name="NYC bars") m

2022-01-22 15:11:15 Projected GeoDataFrame to +proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:11:15 Projected GeoDataFrame to epsg:4326
2022-01-22 15:11:15 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:11:15 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:15 Pausing 1 seconds before making HTTP POST request
2022-01-22 15:11:16 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.745400+-73.996500+40.745400+-73.973800+40.755100+-73.973800+40.755100+-73.996500+40.745400+-73.996500%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.745400+-73.996500+40.745400+-73.973800+40.755100+-73.973800+40.755100+-73.996500+40.745400+-73.996500%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.745400+-73.996500+40.745400+-73.973800+40.755100+-73.973800+40.755100+-73.996500+40.745400+-73.996500%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:11:16 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:18 Downloaded 16.1kB from overpass-api.de
2022-01-22 15:11:18 Saved response to cache file "cache/fc1b84bb839adfa1ccfe180041a3a3ac4a5fb27b.json"
2022-01-22 15:11:18 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:11:18 Converting 49 elements in JSON responses to geometries
2022-01-22 15:11:18 39 geometries created in the dict
2022-01-22 15:11:18 0 untagged geometries removed
2022-01-22 15:11:18 Created r-tree spatial index for 39 geometries
2022-01-22 15:11:18 Identified 39 geometries inside polygon
2022-01-22 15:11:18 0 geometries removed by the polygon filter
2022-01-22 15:11:18 3 geometries removed by the tag filter
2022-01-22 15:11:18 36 geometries in the final GeoDataFrame

Out[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add OSM entities within some distance N, S, E, W of a point to the map.

In [10]:

m = leafmap.Map(center=[46.7808, -96.0156], zoom=12, toolbar_control=False, layers_control=True)
m.add_osm_from_point(center_point=(46.7808, -96.0156), tags={"natural": "water"}, dist=10000, layer_name="Lakes")
m

m = leafmap.Map(center=[46.7808, -96.0156], zoom=12, toolbar_control=False, layers_control=True) m.add_osm_from_point(center_point=(46.7808, -96.0156), tags={"natural": "water"}, dist=10000, layer_name="Lakes") m

2022-01-22 15:11:18 Created bbox 10000 m from (46.7808, -96.0156): 46.87073203354929,46.69086796645071,-95.88427235704086,-96.14692764295916
2022-01-22 15:11:18 Projected GeoDataFrame to +proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:11:18 Projected GeoDataFrame to epsg:4326
2022-01-22 15:11:18 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:11:18 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:19 Pausing 7 seconds before making HTTP POST request
2022-01-22 15:11:26 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27natural%27%3D%27water%27%5D%28poly%3A%2746.690868+-96.146928+46.690868+-95.884272+46.870732+-95.884272+46.870732+-96.146928+46.690868+-96.146928%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27natural%27%3D%27water%27%5D%28poly%3A%2746.690868+-96.146928+46.690868+-95.884272+46.870732+-95.884272+46.870732+-96.146928+46.690868+-96.146928%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27natural%27%3D%27water%27%5D%28poly%3A%2746.690868+-96.146928+46.690868+-95.884272+46.870732+-95.884272+46.870732+-96.146928+46.690868+-96.146928%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:11:26 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:29 Downloaded 3,524.4kB from overpass-api.de
2022-01-22 15:11:29 Saved response to cache file "cache/7e1b9b17a87fd207fe2ef4bba35526a26b941cf3.json"
2022-01-22 15:11:29 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:11:29 Converting 34129 elements in JSON responses to geometries
2022-01-22 15:11:29 648 geometries created in the dict
2022-01-22 15:11:29 67 untagged geometries removed
2022-01-22 15:11:29 Created r-tree spatial index for 581 geometries
2022-01-22 15:11:29 Identified 581 geometries inside polygon
2022-01-22 15:11:29 0 geometries removed by the polygon filter
2022-01-22 15:11:29 9 geometries removed by the tag filter
2022-01-22 15:11:29 572 geometries in the final GeoDataFrame

Out[10]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [11]:

m = leafmap.Map(center=[39.9170, 116.3908], zoom=15, toolbar_control=False, layers_control=True)
m.add_osm_from_point(center_point=(39.9170, 116.3908), tags={"building": True, "natural": "water"}, dist=1000, layer_name="Beijing")
m

m = leafmap.Map(center=[39.9170, 116.3908], zoom=15, toolbar_control=False, layers_control=True) m.add_osm_from_point(center_point=(39.9170, 116.3908), tags={"building": True, "natural": "water"}, dist=1000, layer_name="Beijing") m

2022-01-22 15:11:30 Created bbox 1000 m from (39.917, 116.3908): 39.92599320335493,39.908006796645076,116.40252555265246,116.37907444734753
2022-01-22 15:11:30 Projected GeoDataFrame to +proj=utm +zone=50 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:11:30 Projected GeoDataFrame to epsg:4326
2022-01-22 15:11:30 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:11:30 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:11:30 Pausing 27 seconds before making HTTP POST request
2022-01-22 15:11:57 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27building%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27building%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27building%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28node%5B%27natural%27%3D%27water%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27natural%27%3D%27water%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27natural%27%3D%27water%27%5D%28poly%3A%2739.908007+116.379074+39.908007+116.402526+39.925993+116.402526+39.925993+116.379074+39.908007+116.379074%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:11:57 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:12:01 Downloaded 1,950.6kB from overpass-api.de
2022-01-22 15:12:01 Saved response to cache file "cache/18c0b298eadc540f67bd395df11ce3a66c601ba8.json"
2022-01-22 15:12:01 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:12:01 Converting 17087 elements in JSON responses to geometries
2022-01-22 15:12:01 2734 geometries created in the dict
2022-01-22 15:12:01 78 untagged geometries removed

/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geometries.py:805: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  for merged_outer_linestring in list(merged_outer_linestrings):
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geometries.py:805: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  for merged_outer_linestring in list(merged_outer_linestrings):

2022-01-22 15:12:01 Created r-tree spatial index for 2656 geometries
2022-01-22 15:12:01 Identified 2655 geometries inside polygon
2022-01-22 15:12:01 1 geometries removed by the polygon filter
2022-01-22 15:12:01 39 geometries removed by the tag filter
2022-01-22 15:12:01 2617 geometries in the final GeoDataFrame

Out[11]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add OSM entities within the current map view to the map.

In [12]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.set_center(-73.9854, 40.7500, 16)
m

m = leafmap.Map(toolbar_control=False, layers_control=True) m.set_center(-73.9854, 40.7500, 16) m

Out[12]:

Make this Notebook Trusted to load map: File -> Trust Notebook

The add_osm_from_view() is only available for the ipyleaflet plotting backend.

In [13]:

# m.add_osm_from_view(tags={"amenity": "bar", "building": True}, layer_name="New York")

# m.add_osm_from_view(tags={"amenity": "bar", "building": True}, layer_name="New York")

Create a GeoPandas GeoDataFrame from place.

In [14]:

gdf = leafmap.osm_gdf_from_place("New York City", tags={"amenity": "bar"})
gdf

gdf = leafmap.osm_gdf_from_place("New York City", tags={"amenity": "bar"}) gdf

2022-01-22 15:12:03 Configured OSMnx 1.1.2
2022-01-22 15:12:03 HTTP response caching is on
2022-01-22 15:12:03 Retrieved response from cache file "cache/461ac14500db314f1655f9539ed600906dcbe1da.json"
2022-01-22 15:12:03 Created GeoDataFrame with 1 rows from 1 queries
2022-01-22 15:12:03 Constructed place geometry polygon(s) to query API
2022-01-22 15:12:03 Projected GeoDataFrame to +proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-22 15:12:03 Projected GeoDataFrame to epsg:4326
2022-01-22 15:12:03 Requesting data within polygon from API in 1 request(s)
2022-01-22 15:12:03 Resolved overpass-api.de to 178.63.48.217

/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

2022-01-22 15:12:04 Pausing 5 seconds before making HTTP POST request
2022-01-22 15:12:09 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.477399+-74.228153+40.481241+-74.237935+40.482342+-74.240691+40.486035+-74.249934+40.487386+-74.253313+40.489006+-74.254289+40.497207+-74.259090+40.500983+-74.259090+40.502890+-74.259089+40.507905+-74.258291+40.541851+-74.250609+40.549200+-74.247415+40.630903+-74.202247+40.637648+-74.195664+40.916179+-73.919289+40.910047+-73.851385+40.909994+-73.851134+40.871721+-73.748060+40.752427+-73.701577+40.747036+-73.700987+40.739549+-73.700181+40.594644+-73.735116+40.593898+-73.735333+40.532633+-73.758820+40.493211+-73.903722+40.477399+-74.228153%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.477399+-74.228153+40.481241+-74.237935+40.482342+-74.240691+40.486035+-74.249934+40.487386+-74.253313+40.489006+-74.254289+40.497207+-74.259090+40.500983+-74.259090+40.502890+-74.259089+40.507905+-74.258291+40.541851+-74.250609+40.549200+-74.247415+40.630903+-74.202247+40.637648+-74.195664+40.916179+-73.919289+40.910047+-73.851385+40.909994+-73.851134+40.871721+-73.748060+40.752427+-73.701577+40.747036+-73.700987+40.739549+-73.700181+40.594644+-73.735116+40.593898+-73.735333+40.532633+-73.758820+40.493211+-73.903722+40.477399+-74.228153%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.477399+-74.228153+40.481241+-74.237935+40.482342+-74.240691+40.486035+-74.249934+40.487386+-74.253313+40.489006+-74.254289+40.497207+-74.259090+40.500983+-74.259090+40.502890+-74.259089+40.507905+-74.258291+40.541851+-74.250609+40.549200+-74.247415+40.630903+-74.202247+40.637648+-74.195664+40.916179+-73.919289+40.910047+-73.851385+40.909994+-73.851134+40.871721+-73.748060+40.752427+-73.701577+40.747036+-73.700987+40.739549+-73.700181+40.594644+-73.735116+40.593898+-73.735333+40.532633+-73.758820+40.493211+-73.903722+40.477399+-74.228153%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-22 15:12:09 Resolved overpass-api.de to 178.63.48.217
2022-01-22 15:12:22 Downloaded 442.6kB from overpass-api.de
2022-01-22 15:12:22 Saved response to cache file "cache/2aa3e6d58ab51fc117eb4494f3e4f16a66df0d0a.json"
2022-01-22 15:12:22 Got all geometries data within polygon from API in 1 request(s)
2022-01-22 15:12:22 Converting 1474 elements in JSON responses to geometries
2022-01-22 15:12:23 1063 geometries created in the dict
2022-01-22 15:12:23 0 untagged geometries removed
2022-01-22 15:12:23 Created r-tree spatial index for 1063 geometries
2022-01-22 15:12:23 Identified 1024 geometries inside polygon
2022-01-22 15:12:23 39 geometries removed by the polygon filter
2022-01-22 15:12:23 11 geometries removed by the tag filter
2022-01-22 15:12:23 1013 geometries in the final GeoDataFrame

Out[14]:

		addr:city	addr:country	addr:housenumber	addr:postcode	addr:state	addr:street	amenity	ele	gnis:county_name	gnis:feature_id	...	disused:live_music	disused:amenity	screen	drink:cocktails	building:colour	roof:material	roof:shape	billiards	ways	type
element_type	osmid
node	368043598	New York	US	53	10014	NY	Christopher Street	bar	7	New York	2082946	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	419366609	NaN	NaN	NaN	NaN	NaN	NaN	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	419367969	NaN	NaN	NaN	NaN	NaN	NaN	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	484078905	Brooklyn	NaN	388	11211	NY	Union Avenue	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	617740843	NaN	NaN	NaN	NaN	NaN	NaN	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way	664632831	NaN	NaN	1840	10128	NaN	2nd Avenue	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	664910637	Brooklyn	NaN	3	11222	NY	Milton Street	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	665897391	New York	NaN	54	10001	NaN	West 31st Street	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	763525023	NaN	NaN	183	NaN	NaN	East 86th Street	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16	NaN	NaN
relation	12554650	NaN	NaN	NaN	NaN	NaN	NaN	bar	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	[278963046, 278963089]	multipolygon

1013 rows × 169 columns

---

Last update: 2022-01-22